An optimization study (or study for short) represents an optimization initiative aimed at optimizing a goal on a target system. A study instructs Akamas about the space to explore and the KPIs used to evaluate whether a con configuration is good or bad
Akamas supports two types of optimizations:
Offline Optimization Studies are optimization studies where the workload is simulated by leveraging a load-testing tool.
Live Optimization Studies are applied to systems that need to be optimized in production with respect to varying workloads observed while running live. For example, a microservices application can be optimized live by having Kubernetes and JVM parameters dynamically tuned for multiple microservices so as to minimize costs while matching response time objectives.
A study is described by the following properties
system: the under optimization
parameters: the set of being optimized
metrics: the set of to be collected
The construct to be used to define an optimization is described on the page.
An optimization study is an that can be managed via CLI using the
The Akamas UI shows optimization studies in 2 specific top-level menus: one for offline optimization studies and another for live optimization studies.
goal: the desired optimization goal to be achieved
constraints: the optimization constraints that any configuration needs to satisfy
steps: the steps that are executed to run specific configurations (e.g. the baseline) and run the optimization